code: def __init__(
      self,
      optimizer: Optimizer,
      model: Module,
      use_pipeline: bool,
      param_info: OrderedDict,
      initial_scale: int = 2**16,  # grad scaler config
      min_scale: int = 1,
      growth_factor: float = 2.0,
      backoff_factor: float = 0.5,
      growth_interval: int = 2000,
      hysteresis: int = 2,
      max_scale: int = 2**24,
      clip_grad_norm: float = 0.0,  # grad clipping
      verbose: bool = False,
      reduce_bucket_size: int = 1024 * 1024,  # communication
      communication_dtype: Optional[torch.dtype] = None,
      overlap_communication: bool = True,
      partition_grad: bool = False,  # stage 2 flag
      cpu_offload: bool = False,  # cpu offload
      dp_process_group: Optional[
          ProcessGroup
      ] = None,  # the dp pg for comm
      tp_process_group: Optional[ProcessGroup] = None,  # if using tp
      pp_process_group: Optional[ProcessGroup] = None,
      forced_dtype: Optional[torch.dtype] = None,
      moe_extra_dp_process_group: Optional[ProcessGroup] = None,
  ):
      self.param_info = param_info
      self.stage_manager = model.stage_manager
      self.shared_params = model.shared_params
      self.dp_pg = dp_process_group
      self.tp_pg = tp_process_group
      self.pp_pg = pp_process_group
      if use_pipeline:
          init_pipeline_optimizer(optimizer, model)
      super().__init__(
          optimizer=optimizer,
          initial_scale=initial_scale,
          min_scale=min_scale,
          growth_factor=growth_factor,
          backoff_factor=backoff_factor,
          growth_interval=growth_interval,
          hysteresis=hysteresis,
          max_scale=max_scale,
          clip_grad_norm=clip_grad_norm,
          verbose=verbose,
          reduce_bucket_size=reduce_bucket_size,
          communication_dtype=communication_dtype,
          overlap_communication=overlap_communication,
          partition_grad=partition_grad,
          cpu_offload=cpu_offload,
          dp_process_group=dp_process_group,
          forced_dtype=forced_dtype,
          moe_extra_dp_process_group=moe_extra_dp_process_group,
      )
  
imports: import random
  from types import MethodType
  from typing import Callable, Optional, OrderedDict, Tuple
  import numpy
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from colossalai.booster.plugin.hybrid_parallel_plugin import HybridParallelAMPOptimizer, HybridParallelModule, HybridParallelNaiveOptimizer, HybridParallelPlugin, get_param_info, init_pipeline_optimizer
  from colossalai.cluster import ProcessGroupMesh
  from colossalai.interface import ModelWrapper, OptimizerWrapper
  from colossalai.moe import MoECheckpintIO
  from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer import ShardConfig
  from colossalai.shardformer.policies.base_policy import Policy
  from colossalai.zero.low_level import LowLevelZeroOptimizer
label_desc: Use image node_id 1 to create a new HybridParallelZeroOptimizer object from inherited base classes: LowLevelZeroOptimizer with example: obj = HybridParallelZeroOptimizer(optimizer, model, use_pipeline, param_info, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, max_scale, clip_grad_norm, verbose, reduce_bucket_size, communication_dtype, overlap_communication, partition_grad, cpu_offload, dp_process_group, tp_process_group, pp_process_group, forced_dtype, moe_extra_dp_process_group)